Pooling, backward and forward selection of linear, logistic and Cox regression models in
multiply imputed datasets. Backward and forward selection can be done
from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and
the median p-values method. This is also possible for Mixed models.
The models can contain continuous, dichotomous, categorical and restricted
cubic spline predictors and interaction terms between all these type of predictors.
The stability of the models can be evaluated using bootstrapping and cluster
bootstrapping. The package further contains functions to pool the model performance
as ROC/AUC, R-squares, scaled Brier score, H&L test and calibration plots for logistic
regression models. Internal validation can be done with cross-validation or bootstrapping.
The adjusted intercept after shrinkage of pooled regression coefficients can be obtained.
Backward and forward selection as part of internal validation is possible.
A function to externally validate logistic prediction models in multiple imputed
datasets is available and a function to compare models.